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+#!/usr/bin/env python
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+# -*- coding: UTF-8 -*-
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+#
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+# Copyright (C) 2009-2015 Ovidio Peña Rodríguez <ovidio@bytesfall.com>
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+#
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+# This file is part of python-scattnlay
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+#
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+# This program is free software: you can redistribute it and/or modify
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+# it under the terms of the GNU General Public License as published by
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+# the Free Software Foundation, either version 3 of the License, or
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+# (at your option) any later version.
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+#
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+# This program is distributed in the hope that it will be useful,
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+# but WITHOUT ANY WARRANTY; without even the implied warranty of
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+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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+# GNU General Public License for more details.
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+#
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+# The only additional remark is that we expect that all publications
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+# describing work using this software, or all commercial products
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+# using it, cite the following reference:
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+# [1] O. Pena and U. Pal, "Scattering of electromagnetic radiation by
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+# a multilayered sphere," Computer Physics Communications,
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+# vol. 180, Nov. 2009, pp. 2348-2354.
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+#
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+# You should have received a copy of the GNU General Public License
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+# along with this program. If not, see <http://www.gnu.org/licenses/>.
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+
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+# This test case calculates the electric field in the
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+# E-k plane, for an spherical Si-Ag-Si nanoparticle. Core radius is 17.74 nm,
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+# inner layer 23.31nm, outer layer 22.95nm. Working wavelength is 800nm, we use
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+# silicon epsilon=13.64+i0.047, silver epsilon= -28.05+i1.525
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+
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+import scattnlay
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+from scattnlay import fieldnlay
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+from scattnlay import scattnlay
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+import numpy as np
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+import cmath
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+
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+
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+def get_index(array,value):
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+ idx = (np.abs(array-value)).argmin()
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+ return idx
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+
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+#Ec = np.resize(Ec, (npts, npts)).T
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+
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+
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+def GetFlow(scale_x, scale_z, Ec, Hc, a, b, nmax):
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+ # Initial position
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+ flow_x = [a]
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+ flow_z = [b]
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+ x_pos = flow_x[-1]
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+ z_pos = flow_z[-1]
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+ x_idx = get_index(scale_x, x_pos)
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+ z_idx = get_index(scale_z, z_pos)
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+ S=np.cross(Ec[npts*z_idx+x_idx], Hc[npts*z_idx+x_idx]).real
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+ #if (np.linalg.norm(S)> 1e-4):
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+ Snorm_prev=S/np.linalg.norm(S)
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+ for n in range(0, nmax):
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+ #Get the next position
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+ #1. Find Poynting vector and normalize it
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+ x_pos = flow_x[-1]
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+ z_pos = flow_z[-1]
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+ x_idx = get_index(scale_x, x_pos)
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+ z_idx = get_index(scale_z, z_pos)
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+ #print x_idx, z_idx
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+ S=np.cross(Ec[npts*z_idx+x_idx], Hc[npts*z_idx+x_idx]).real
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+ #if (np.linalg.norm(S)> 1e-4):
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+ Snorm=S/np.linalg.norm(S)
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+ #2. Evaluate displacement = half of the discrete and new position
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+ dpos = abs(scale_z[0]-scale_z[1])/2.0
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+ dx = dpos*Snorm[0]
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+ dz = dpos*Snorm[2]
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+ x_pos = x_pos+dx
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+ z_pos = z_pos+dz
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+ #3. Save result
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+ flow_x.append(x_pos)
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+ flow_z.append(z_pos)
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+ return flow_x, flow_z
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+
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+# # a)
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+#WL=400 #nm
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+#core_r = WL/20.0
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+#epsilon_Ag = -2.0 + 10.0j
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+
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+# # b)
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+#WL=400 #nm
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+#core_r = WL/20.0
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+#epsilon_Ag = -2.0 + 1.0j
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+
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+# c)
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+WL=354 #nm
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+core_r = WL/20.0
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+epsilon_Ag = -2.0 + 0.28j
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+
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+# # d)
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+# WL=367 #nm
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+# core_r = WL/20.0
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+# epsilon_Ag = -2.71 + 0.25j
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+
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+
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+index_Ag = np.sqrt(epsilon_Ag)
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+print(index_Ag)
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+
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+
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+# n1 = 1.53413
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+# n2 = 0.565838 + 7.23262j
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+nm = 1.0
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+
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+x = np.ones((1, 1), dtype = np.float64)
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+x[0, 0] = 2.0*np.pi*core_r/WL
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+
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+m = np.ones((1, 1), dtype = np.complex128)
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+m[0, 0] = index_Ag/nm
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+
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+print "x =", x
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+print "m =", m
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+
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+npts = 281
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+
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+factor=2
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+scan = np.linspace(-factor*x[0, 0], factor*x[0, 0], npts)
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+
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+coord = np.zeros((npts, 3), dtype = np.float64)
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+coord[:, 2] = scan
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+
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+terms, Qext, Qsca, Qabs, Qbk, Qpr, g, Albedo, S1, S2 = scattnlay(x, m)
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+terms, E, H = fieldnlay(x, m, coord)
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+
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+P = np.array(map(lambda n: np.cross(E[0][n], H[0][n])[2].real, range(0, len(E[0]))))
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+
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+Ec = E[0, :, :]
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+Hc = H[0, :, :]
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+
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+result = np.vstack((scan, P)).transpose()
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+
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+try:
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+ import matplotlib.pyplot as plt
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+
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+ fig = plt.figure()
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+ ax = fig.add_subplot(111)
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+
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+ ax.errorbar(result[:, 0], result[:, 1], fmt = 'r', label = 'X axis')
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+
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+ ax.legend()
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+
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+ plt.xlabel('X')
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+# plt.ylabel('|P|/|Eo|')
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+
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+ plt.draw()
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+ plt.show()
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+finally:
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+ np.savetxt("lfield.txt", result, fmt = "%.5f")
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+ print result
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+
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+
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+# try:
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+# import matplotlib.pyplot as plt
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+# from matplotlib import cm
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+# from matplotlib.colors import LogNorm
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+
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+# # min_tick = 0.0
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+# # max_tick = 1.0
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+
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+# Eabs_data = np.resize(P, (npts, npts)).T
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+
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+# #Eabs_data = np.resize(Eabs, (npts, npts)).T
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+# #Eabs_data = np.resize(Eangle, (npts, npts)).T
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+# #Eabs_data = np.resize(Habs, (npts, npts)).T
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+# #Eabs_data = np.resize(Hangle, (npts, npts)).T
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+
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+# fig, ax = plt.subplots(1,1)#, sharey=True, sharex=True)
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+# #fig.tight_layout()
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+# # Rescale to better show the axes
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+# scale_x = np.linspace(min(coordX)*WL/2.0/np.pi/nm, max(coordX)*WL/2.0/np.pi/nm, npts)
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+# scale_z = np.linspace(min(coordZ)*WL/2.0/np.pi/nm, max(coordZ)*WL/2.0/np.pi/nm, npts)
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+
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+# # Define scale ticks
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+# min_tick = np.amin(Eabs_data)
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+# max_tick = np.amax(Eabs_data)
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+# # scale_ticks = np.power(10.0, np.linspace(np.log10(min_tick), np.log10(max_tick), 6))
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+# scale_ticks = np.linspace(min_tick, max_tick, 11)
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+
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+# # Interpolation can be 'nearest', 'bilinear' or 'bicubic'
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+# #ax.set_title('Eabs')
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+# cax = ax.imshow(Eabs_data, interpolation = 'nearest', cmap = cm.jet,
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+# origin = 'lower'
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+# #, vmin = min_tick, vmax = max_tick
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+# , extent = (min(scale_x), max(scale_x), min(scale_z), max(scale_z))
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+# #,norm = LogNorm()
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+# )
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+# ax.axis("image")
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+
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+# # # Add colorbar
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+# cbar = fig.colorbar(cax, ticks = [a for a in scale_ticks])
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+# cbar.ax.set_yticklabels(['%5.3g' % (a) for a in scale_ticks]) # vertically oriented colorbar
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+# # pos = list(cbar.ax.get_position().bounds)
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+# # fig.text(pos[0] - 0.02, 0.925, '|E|/|E$_0$|', fontsize = 14)
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+
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+# plt.xlabel('Z, nm')
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+# plt.ylabel('X, nm')
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+
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+# # This part draws the nanoshell
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+# from matplotlib import patches
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+# s1 = patches.Arc((0, 0), 2.0*core_r, 2.0*core_r, angle=0.0, zorder=2,
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+# theta1=0.0, theta2=360.0, linewidth=1, color='black')
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+# ax.add_patch(s1)
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+
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+# from matplotlib.path import Path
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+# #import matplotlib.patches as patches
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+# flow_total = 41
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+# for flow in range(0,flow_total):
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+# flow_x, flow_z = GetFlow(scale_x, scale_z, Ec, Hc,
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+# min(scale_x)+flow*(scale_x[-1]-scale_x[0])/(flow_total-1),
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+# min(scale_z),
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+# npts*6)
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+# verts = np.vstack((flow_z, flow_x)).transpose().tolist()
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+# #codes = [Path.CURVE4]*len(verts)
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+# codes = [Path.LINETO]*len(verts)
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+# codes[0] = Path.MOVETO
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+# path = Path(verts, codes)
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+# patch = patches.PathPatch(path, facecolor='none', lw=1, edgecolor='white')
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+# ax.add_patch(patch)
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+# # # Start powerflow lines in the middle of the image
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+# # flow_total = 131
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+# # for flow in range(0,flow_total):
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+# # flow_x, flow_z = GetFlow(scale_x, scale_z, Ec, Hc,
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+# # min(scale_x)+flow*(scale_x[-1]-scale_x[0])/(flow_total-1),
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+# # 15.0, #min(scale_z),
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+# # npts*6)
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+# # verts = np.vstack((flow_z, flow_x)).transpose().tolist()
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+# # #codes = [Path.CURVE4]*len(verts)
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+# # codes = [Path.LINETO]*len(verts)
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+# # codes[0] = Path.MOVETO
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+# # path = Path(verts, codes)
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+# # patch = patches.PathPatch(path, facecolor='none', lw=1, edgecolor='white')
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+# # ax.add_patch(patch)
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+
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+# plt.savefig("Ag-flow.png")
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+# plt.draw()
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+
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+# plt.show()
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+
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+# plt.clf()
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+# plt.close()
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+# finally:
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+# print("Qabs = "+str(Qabs));
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+# #
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+
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+
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